


'''simple docstring'''




import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import (
           MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
           VideoMAEForPreTraining,
           VideoMAEForVideoClassification,
           VideoMAEModel,
       )
       from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from transformers import VideoMAEImageProcessor




class 			_lowercase :
   def __init__(						self       :      int       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      Dict=13       ,   SCREAMING_SNAKE_CASE_       :      Any=10       ,   SCREAMING_SNAKE_CASE_       :      List[str]=3       ,   SCREAMING_SNAKE_CASE_       :      int=2       ,   SCREAMING_SNAKE_CASE_       :      Dict=2       ,   SCREAMING_SNAKE_CASE_       :      Dict=2       ,   SCREAMING_SNAKE_CASE_       :      List[str]=True       ,   SCREAMING_SNAKE_CASE_       :      str=True       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=32       ,   SCREAMING_SNAKE_CASE_       :      Any=5       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=4       ,   SCREAMING_SNAKE_CASE_       :      Tuple=37       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]="gelu"       ,   SCREAMING_SNAKE_CASE_       :      Any=0.1       ,   SCREAMING_SNAKE_CASE_       :      str=0.1       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=10       ,   SCREAMING_SNAKE_CASE_       :      Tuple=0.0_2       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=0.9       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=None       ,   ) ->							Union[str, Any]:
        __snake_case           =						parent
        __snake_case           =						batch_size
        __snake_case           =						image_size
        __snake_case           =						num_channels
        __snake_case           =						patch_size
        __snake_case           =						tubelet_size
        __snake_case           =						num_frames
        __snake_case           =						is_training
        __snake_case           =						use_labels
        __snake_case           =						hidden_size
        __snake_case           =						num_hidden_layers
        __snake_case           =						num_attention_heads
        __snake_case           =						intermediate_size
        __snake_case           =						hidden_act
        __snake_case           =						hidden_dropout_prob
        __snake_case           =						attention_probs_dropout_prob
        __snake_case           =						type_sequence_label_size
        __snake_case           =						initializer_range
        __snake_case           =						mask_ratio
        __snake_case           =						scope

        # in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
        __snake_case           =						(image_size // patch_size) ** 2
        __snake_case           =						(num_frames // tubelet_size) * self.num_patches_per_frame

        # use this variable to define bool_masked_pos
        __snake_case           =						int(mask_ratio * self.seq_length      )
   def a  (						self       :      List[str]      ) ->							Optional[Any]:
        __snake_case           =						floats_tensor(
            [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]      )

        __snake_case           =						None
        if self.use_labels:
             __snake_case           =						ids_tensor([self.batch_size]       ,   self.type_sequence_label_size      )

        __snake_case           =						self.get_config()

        return config, pixel_values, labels
   def a  (						self       :      Union[str, Any]      ) ->							Optional[Any]:
        return VideoMAEConfig(
            image_size=self.image_size       ,   patch_size=self.patch_size       ,   num_channels=self.num_channels       ,   num_frames=self.num_frames       ,   tubelet_size=self.tubelet_size       ,   hidden_size=self.hidden_size       ,   num_hidden_layers=self.num_hidden_layers       ,   num_attention_heads=self.num_attention_heads       ,   intermediate_size=self.intermediate_size       ,   hidden_act=self.hidden_act       ,   hidden_dropout_prob=self.hidden_dropout_prob       ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,   is_decoder=SCREAMING_SNAKE_CASE_       ,   initializer_range=self.initializer_range       ,   )
   def a  (						self       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      int       ,   SCREAMING_SNAKE_CASE_       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      List[Any]      ) ->							Any:
        __snake_case           =						VideoMAEModel(config=SCREAMING_SNAKE_CASE_      )
        model.to(SCREAMING_SNAKE_CASE_      )
        model.eval()
        __snake_case           =						model(SCREAMING_SNAKE_CASE_      )
        self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, self.seq_length, self.hidden_size)      )
   def a  (						self       :      str       ,   SCREAMING_SNAKE_CASE_       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      Any      ) ->							List[str]:
        __snake_case           =						VideoMAEForPreTraining(SCREAMING_SNAKE_CASE_      )
        model.to(SCREAMING_SNAKE_CASE_      )
        model.eval()
        # important: each video needs to have the same number of masked patches
        # hence we define a single mask, which we then repeat for each example in the batch
        __snake_case           =						torch.ones((self.num_masks,)      )
        __snake_case           =						torch.cat([mask, torch.zeros(self.seq_length - mask.size(0      )      )]      )
        __snake_case           =						mask.expand(self.batch_size       ,   -1      ).bool()

        __snake_case           =						model(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )
        # model only returns predictions for masked patches
        __snake_case           =						mask.sum().item()
        __snake_case           =						3 * self.tubelet_size * self.patch_size**2
        self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, num_masked_patches, decoder_num_labels)      )





   def a  (						self       :      List[str]      ) ->							Any:
        __snake_case           =						self.prepare_config_and_inputs()
        __snake_case      ,       __snake_case      ,       __snake_case           =						config_and_inputs
        __snake_case           =						{'pixel_values': pixel_values}
        return config, inputs_dict




@require_torch
class 			_lowercase (	__lowercase						,					__lowercase						,					unittest.TestCase							):
   _SCREAMING_SNAKE_CASE  :  Optional[Any]          =	(
       (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
   )
   _SCREAMING_SNAKE_CASE  :  int          =	(
       {"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
       if is_torch_available()
       else {}
   )

   _SCREAMING_SNAKE_CASE  :  str          =	False
   _SCREAMING_SNAKE_CASE  :  Optional[Any]          =	False
   _SCREAMING_SNAKE_CASE  :  List[Any]          =	False
   _SCREAMING_SNAKE_CASE  :  Tuple          =	False
   def a  (						self       :      List[str]      ) ->							Union[str, Any]:
        __snake_case           =						VideoMAEModelTester(self      )
        __snake_case           =						ConfigTester(self       ,   config_class=SCREAMING_SNAKE_CASE_       ,   has_text_modality=SCREAMING_SNAKE_CASE_       ,   hidden_size=37      )
   def a  (						self       :      Dict       ,   SCREAMING_SNAKE_CASE_       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=False      ) ->							Any:
        __snake_case           =						copy.deepcopy(SCREAMING_SNAKE_CASE_      )

        if model_class == VideoMAEForPreTraining:
             # important: each video needs to have the same number of masked patches
             # hence we define a single mask, which we then repeat for each example in the batch
             __snake_case           =						torch.ones((self.model_tester.num_masks,)      )
             __snake_case           =						torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0      )      )]      )
             __snake_case           =						mask.expand(self.model_tester.batch_size       ,   -1      ).bool()
             __snake_case           =						bool_masked_pos.to(SCREAMING_SNAKE_CASE_      )

        if return_labels:
             if model_class in [
                 *get_values(SCREAMING_SNAKE_CASE_      ),
             ]:
                  __snake_case           =						torch.zeros(
                      self.model_tester.batch_size       ,   dtype=torch.long       ,   device=SCREAMING_SNAKE_CASE_      )

        return inputs_dict
   def a  (						self       :      List[Any]      ) ->							List[Any]:
        self.config_tester.run_common_tests()
   @unittest.skip(reason='VideoMAE does not use inputs_embeds'      )
   def a  (						self       :      Tuple      ) ->							str:
        pass
   def a  (						self       :      Optional[Any]      ) ->							Optional[int]:
        __snake_case      ,       __snake_case           =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             __snake_case           =						model_class(SCREAMING_SNAKE_CASE_      )
             self.assertIsInstance(model.get_input_embeddings()       ,   (nn.Module)      )
             __snake_case           =						model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_       ,   nn.Linear      )      )
   def a  (						self       :      List[Any]      ) ->							Tuple:
        __snake_case      ,       __snake_case           =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             __snake_case           =						model_class(SCREAMING_SNAKE_CASE_      )
             __snake_case           =						inspect.signature(model.forward      )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             __snake_case           =						[*signature.parameters.keys()]

             __snake_case           =						['pixel_values']
             self.assertListEqual(arg_names[:1]       ,   SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Dict      ) ->							Union[str, Any]:
        __snake_case           =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Tuple      ) ->							Optional[int]:
        __snake_case           =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_      )
   @slow
   def a  (						self       :      Dict      ) ->							Any:
        for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __snake_case           =						VideoMAEModel.from_pretrained(SCREAMING_SNAKE_CASE_      )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Optional[Any]      ) ->							Optional[Any]:
        if not self.has_attentions:
             pass

        else:
             __snake_case      ,       __snake_case           =						self.model_tester.prepare_config_and_inputs_for_common()
             __snake_case           =						True

             for model_class in self.all_model_classes:
                  __snake_case           =						self.model_tester.seq_length - self.model_tester.num_masks
                  __snake_case           =						(
                      num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
                  )

                  __snake_case           =						True
                  __snake_case           =						False
                  __snake_case           =						True
                  __snake_case           =						model_class(SCREAMING_SNAKE_CASE_      )
                  model.to(SCREAMING_SNAKE_CASE_      )
                  model.eval()
                  with torch.no_grad():
                       __snake_case           =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )      )
                  __snake_case           =						outputs.attentions
                  self.assertEqual(len(SCREAMING_SNAKE_CASE_      )       ,   self.model_tester.num_hidden_layers      )

                  # check that output_attentions also work using config
                  del inputs_dict["output_attentions"]
                  __snake_case           =						True
                  __snake_case           =						model_class(SCREAMING_SNAKE_CASE_      )
                  model.to(SCREAMING_SNAKE_CASE_      )
                  model.eval()
                  with torch.no_grad():
                       __snake_case           =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )      )
                  __snake_case           =						outputs.attentions
                  self.assertEqual(len(SCREAMING_SNAKE_CASE_      )       ,   self.model_tester.num_hidden_layers      )

                  self.assertListEqual(
                      list(attentions[0].shape[-3:]      )       ,   [self.model_tester.num_attention_heads, seq_len, seq_len]       ,   )
                  __snake_case           =						len(SCREAMING_SNAKE_CASE_      )

                  # Check attention is always last and order is fine
                  __snake_case           =						True
                  __snake_case           =						True
                  __snake_case           =						model_class(SCREAMING_SNAKE_CASE_      )
                  model.to(SCREAMING_SNAKE_CASE_      )
                  model.eval()
                  with torch.no_grad():
                       __snake_case           =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )      )

                  self.assertEqual(out_len + 1       ,   len(SCREAMING_SNAKE_CASE_      )      )

                  __snake_case           =						outputs.attentions

                  self.assertEqual(len(SCREAMING_SNAKE_CASE_      )       ,   self.model_tester.num_hidden_layers      )
                  self.assertListEqual(
                      list(self_attentions[0].shape[-3:]      )       ,   [self.model_tester.num_attention_heads, seq_len, seq_len]       ,   )
   def a  (						self       :      List[str]      ) ->							str:
        def check_hidden_states_output(SCREAMING_SNAKE_CASE_       :      int       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]      ):
             __snake_case           =						model_class(SCREAMING_SNAKE_CASE_      )
             model.to(SCREAMING_SNAKE_CASE_      )
             model.eval()

             with torch.no_grad():
                  __snake_case           =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )      )

             __snake_case           =						outputs.hidden_states
             __snake_case           =						self.model_tester.num_hidden_layers + 1
             self.assertEqual(len(SCREAMING_SNAKE_CASE_      )       ,   SCREAMING_SNAKE_CASE_      )

             __snake_case           =						self.model_tester.seq_length - self.model_tester.num_masks
             __snake_case           =						num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]      )       ,   [seq_length, self.model_tester.hidden_size]       ,   )

        __snake_case      ,       __snake_case           =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             __snake_case           =						True
             check_hidden_states_output(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             __snake_case           =						True

             check_hidden_states_output(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )





   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
   def a  (						self       :      Dict      ) ->							Optional[int]:
        pass





def   _a		()			->			Tuple:






     """simple docstring"""
     __snake_case           =						hf_hub_download(
         repo_id='hf-internal-testing/spaghetti-video'							,   filename='eating_spaghetti.npy'							,   repo_type='dataset'						)
     __snake_case           =						np.load(lowercase__						)
     return list(lowercase__						)




@require_torch
@require_vision
class 			_lowercase (	unittest.TestCase							):
   @cached_property
   def a  (						self       :      Any      ) ->							int:
        # logits were tested with a different mean and std, so we use the same here
        return (
            VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]       ,   image_std=[0.5, 0.5, 0.5]      )
            if is_vision_available()
            else None
        )
   @slow
   def a  (						self       :      Any      ) ->							Dict:
        __snake_case           =						VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics'      ).to(
            SCREAMING_SNAKE_CASE_      )

        __snake_case           =						self.default_image_processor
        __snake_case           =						prepare_video()
        __snake_case           =						image_processor(SCREAMING_SNAKE_CASE_       ,   return_tensors='pt'      ).to(SCREAMING_SNAKE_CASE_      )

        # forward pass
        with torch.no_grad():
             __snake_case           =						model(**SCREAMING_SNAKE_CASE_      )

        # verify the logits
        __snake_case           =						torch.Size((1, 400)      )
        self.assertEqual(outputs.logits.shape       ,   SCREAMING_SNAKE_CASE_      )

        __snake_case           =						torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]      ).to(SCREAMING_SNAKE_CASE_      )

        self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,   SCREAMING_SNAKE_CASE_       ,   atol=1e-4      )      )





   @slow
   def a  (						self       :      Dict      ) ->							Optional[int]:
        __snake_case           =						VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short'      ).to(SCREAMING_SNAKE_CASE_      )

        __snake_case           =						self.default_image_processor
        __snake_case           =						prepare_video()
        __snake_case           =						image_processor(SCREAMING_SNAKE_CASE_       ,   return_tensors='pt'      ).to(SCREAMING_SNAKE_CASE_      )

        # add boolean mask, indicating which patches to mask
        __snake_case           =						hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'       ,   filename='bool_masked_pos.pt'      )
        __snake_case           =						torch.load(SCREAMING_SNAKE_CASE_      )

        # forward pass
        with torch.no_grad():
             __snake_case           =						model(**SCREAMING_SNAKE_CASE_      )

        # verify the logits
        __snake_case           =						torch.Size([1, 1408, 1536]      )
        __snake_case           =						torch.tensor(
            [[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]       ,   device=SCREAMING_SNAKE_CASE_      )
        self.assertEqual(outputs.logits.shape       ,   SCREAMING_SNAKE_CASE_      )
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]       ,   SCREAMING_SNAKE_CASE_       ,   atol=1e-4      )      )

        # verify the loss (`config.norm_pix_loss` = `True`)
        __snake_case           =						torch.tensor([0.5_1_4_2]       ,   device=SCREAMING_SNAKE_CASE_      )
        self.assertTrue(torch.allclose(outputs.loss       ,   SCREAMING_SNAKE_CASE_       ,   atol=1e-4      )      )

        # verify the loss (`config.norm_pix_loss` = `False`)
        __snake_case           =						VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short'       ,   norm_pix_loss=SCREAMING_SNAKE_CASE_      ).to(
            SCREAMING_SNAKE_CASE_      )

        with torch.no_grad():
             __snake_case           =						model(**SCREAMING_SNAKE_CASE_      )

        __snake_case           =						torch.tensor(torch.tensor([0.6_4_6_9]      )       ,   device=SCREAMING_SNAKE_CASE_      )
        self.assertTrue(torch.allclose(outputs.loss       ,   SCREAMING_SNAKE_CASE_       ,   atol=1e-4      )      )





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Dict         =    logging.get_logger(__name__)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				MobileViTConfig()

				# size of the architecture
				if "mobilevit_s" in mobilevit_name:
								__lowercase					     =				[144, 192, 240]
								__lowercase					     =				[16, 32, 64, 96, 128, 160, 640]
				elif "mobilevit_xs" in mobilevit_name:
								__lowercase					     =				[96, 120, 144]
								__lowercase					     =				[16, 32, 48, 64, 80, 96, 384]
				elif "mobilevit_xxs" in mobilevit_name:
								__lowercase					     =				[64, 80, 96]
								__lowercase					     =				[16, 16, 24, 48, 64, 80, 320]
								__lowercase					     =				0.05
								__lowercase					     =				2.0

				if mobilevit_name.startswith('''deeplabv3_'''				):
								__lowercase					     =				512
								__lowercase					     =				16
								__lowercase					     =				21
								__lowercase					     =				'''pascal-voc-id2label.json'''
				else:
								__lowercase					     =				1000
								__lowercase					     =				'''imagenet-1k-id2label.json'''

				__lowercase					     =				'''huggingface/label-files'''
				__lowercase					     =				json.load(open(hf_hub_download(lowercase				,     lowercase				,     repo_type='''dataset'''				)				,     '''r'''				)				)
				__lowercase					     =				{int(lowercase				): v for k, v in idalabel.items()}
				__lowercase					     =				idalabel
				__lowercase					     =				{v: k for k, v in idalabel.items()}

				return config





def 							UpperCAmelCase		(       lowercase				,     lowercase=False				):







				"""simple docstring"""



				for i in range(1				,     6				):
								if F"layer_{i}." in name:
												__lowercase					     =				name.replace(F"layer_{i}."				,     F"encoder.layer.{i - 1}."				)

				if "conv_1." in name:
								__lowercase					     =				name.replace('''conv_1.'''				,     '''conv_stem.'''				)
				if ".block." in name:
								__lowercase					     =				name.replace('''.block.'''				,     '''.'''				)

				if "exp_1x1" in name:
								__lowercase					     =				name.replace('''exp_1x1'''				,     '''expand_1x1'''				)
				if "red_1x1" in name:
								__lowercase					     =				name.replace('''red_1x1'''				,     '''reduce_1x1'''				)
				if ".local_rep.conv_3x3." in name:
								__lowercase					     =				name.replace('''.local_rep.conv_3x3.'''				,     '''.conv_kxk.'''				)
				if ".local_rep.conv_1x1." in name:
								__lowercase					     =				name.replace('''.local_rep.conv_1x1.'''				,     '''.conv_1x1.'''				)
				if ".norm." in name:
								__lowercase					     =				name.replace('''.norm.'''				,     '''.normalization.'''				)
				if ".conv." in name:
								__lowercase					     =				name.replace('''.conv.'''				,     '''.convolution.'''				)
				if ".conv_proj." in name:
								__lowercase					     =				name.replace('''.conv_proj.'''				,     '''.conv_projection.'''				)

				for i in range(0				,     2				):
								for j in range(0				,     4				):
												if F".{i}.{j}." in name:
																__lowercase					     =				name.replace(F".{i}.{j}."				,     F".{i}.layer.{j}."				)

				for i in range(2				,     6				):
								for j in range(0				,     4				):
												if F".{i}.{j}." in name:
																__lowercase					     =				name.replace(F".{i}.{j}."				,     F".{i}."				)
																if "expand_1x1" in name:
																				__lowercase					     =				name.replace('''expand_1x1'''				,     '''downsampling_layer.expand_1x1'''				)
																if "conv_3x3" in name:
																				__lowercase					     =				name.replace('''conv_3x3'''				,     '''downsampling_layer.conv_3x3'''				)
																if "reduce_1x1" in name:
																				__lowercase					     =				name.replace('''reduce_1x1'''				,     '''downsampling_layer.reduce_1x1'''				)

				for i in range(2				,     5				):
								if F".global_rep.{i}.weight" in name:
												__lowercase					     =				name.replace(F".global_rep.{i}.weight"				,     '''.layernorm.weight'''				)
								if F".global_rep.{i}.bias" in name:
												__lowercase					     =				name.replace(F".global_rep.{i}.bias"				,     '''.layernorm.bias'''				)

				if ".global_rep." in name:
								__lowercase					     =				name.replace('''.global_rep.'''				,     '''.transformer.'''				)
				if ".pre_norm_mha.0." in name:
								__lowercase					     =				name.replace('''.pre_norm_mha.0.'''				,     '''.layernorm_before.'''				)
				if ".pre_norm_mha.1.out_proj." in name:
								__lowercase					     =				name.replace('''.pre_norm_mha.1.out_proj.'''				,     '''.attention.output.dense.'''				)
				if ".pre_norm_ffn.0." in name:
								__lowercase					     =				name.replace('''.pre_norm_ffn.0.'''				,     '''.layernorm_after.'''				)
				if ".pre_norm_ffn.1." in name:
								__lowercase					     =				name.replace('''.pre_norm_ffn.1.'''				,     '''.intermediate.dense.'''				)
				if ".pre_norm_ffn.4." in name:
								__lowercase					     =				name.replace('''.pre_norm_ffn.4.'''				,     '''.output.dense.'''				)
				if ".transformer." in name:
								__lowercase					     =				name.replace('''.transformer.'''				,     '''.transformer.layer.'''				)

				if ".aspp_layer." in name:
								__lowercase					     =				name.replace('''.aspp_layer.'''				,     '''.'''				)
				if ".aspp_pool." in name:
								__lowercase					     =				name.replace('''.aspp_pool.'''				,     '''.'''				)
				if "seg_head." in name:
								__lowercase					     =				name.replace('''seg_head.'''				,     '''segmentation_head.'''				)
				if "segmentation_head.classifier.classifier." in name:
								__lowercase					     =				name.replace('''segmentation_head.classifier.classifier.'''				,     '''segmentation_head.classifier.'''				)

				if "classifier.fc." in name:
								__lowercase					     =				name.replace('''classifier.fc.'''				,     '''classifier.'''				)
				elif (not base_model) and ("segmentation_head." not in name):
								__lowercase					     =				'''mobilevit.''' + name

				return name





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase=False				):







				"""simple docstring"""



				if base_model:
								__lowercase					     =				''''''
				else:
								__lowercase					     =				'''mobilevit.'''

				for key in orig_state_dict.copy().keys():
								__lowercase					     =				orig_state_dict.pop(lowercase				)

								if key[:8] == "encoder.":
												__lowercase					     =				key[8:]

								if "qkv" in key:
												__lowercase					     =				key.split('''.'''				)
												__lowercase					     =				int(key_split[0][6:]				) - 1
												__lowercase					     =				int(key_split[3]				)
												__lowercase					     =				model.get_submodule(F"{model_prefix}encoder.layer.{layer_num}"				)
												__lowercase					     =				layer.transformer.layer[transformer_num].attention.attention.all_head_size
												__lowercase					     =				(
												    F"{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."
												)
												if "weight" in key:
																__lowercase					     =				val[:dim, :]
																__lowercase					     =				val[dim : dim * 2, :]
																__lowercase					     =				val[-dim:, :]
												else:
																__lowercase					     =				val[:dim]
																__lowercase					     =				val[dim : dim * 2]
																__lowercase					     =				val[-dim:]
								else:
												__lowercase					     =				val

				return orig_state_dict





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowercase					     =				Image.open(requests.get(lowercase				,     stream=lowercase				).raw				)
				return im





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				,     lowercase=False				):







				"""simple docstring"""



				__lowercase					     =				get_mobilevit_config(lowercase				)

				# load original state_dict
				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)

				# load 🤗 model
				if mobilevit_name.startswith('''deeplabv3_'''				):
								__lowercase					     =				MobileViTForSemanticSegmentation(lowercase				).eval()
				else:
								__lowercase					     =				MobileViTForImageClassification(lowercase				).eval()

				__lowercase					     =				convert_state_dict(lowercase				,     lowercase				)
				model.load_state_dict(lowercase				)

				# Check outputs on an image, prepared by MobileViTImageProcessor
				__lowercase					     =				MobileViTImageProcessor(crop_size=config.image_size				,     size=config.image_size + 32				)
				__lowercase					     =				image_processor(images=prepare_img()				,     return_tensors='''pt'''				)
				__lowercase					     =				model(**lowercase				)
				__lowercase					     =				outputs.logits

				if mobilevit_name.startswith('''deeplabv3_'''				):
								assert logits.shape == (1, 21, 32, 32)

								if mobilevit_name == "deeplabv3_mobilevit_s":
												__lowercase					     =				torch.tensor(
												    [
												        [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
												        [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
												        [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
												    ]				)
								elif mobilevit_name == "deeplabv3_mobilevit_xs":
												__lowercase					     =				torch.tensor(
												    [
												        [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
												        [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
												        [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
												    ]				)
								elif mobilevit_name == "deeplabv3_mobilevit_xxs":
												__lowercase					     =				torch.tensor(
												    [
												        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
												        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
												        [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
												    ]				)
								else:
												raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"				)

								assert torch.allclose(logits[0, :3, :3, :3]				,     lowercase				,     atol=1E-4				)
				else:
								assert logits.shape == (1, 1000)

								if mobilevit_name == "mobilevit_s":
												__lowercase					     =				torch.tensor([-0.9866, 0.2392, -1.1241]				)
								elif mobilevit_name == "mobilevit_xs":
												__lowercase					     =				torch.tensor([-2.4761, -0.9399, -1.9587]				)
								elif mobilevit_name == "mobilevit_xxs":
												__lowercase					     =				torch.tensor([-1.9364, -1.2327, -0.4653]				)
								else:
												raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"				)

								assert torch.allclose(logits[0, :3]				,     lowercase				,     atol=1E-4				)

				Path(lowercase				).mkdir(exist_ok=lowercase				)
				print(F"Saving model {mobilevit_name} to {pytorch_dump_folder_path}"				)
				model.save_pretrained(lowercase				)
				print(F"Saving image processor to {pytorch_dump_folder_path}"				)
				image_processor.save_pretrained(lowercase				)

				if push_to_hub:
								__lowercase					     =				{
								    '''mobilevit_s''': '''mobilevit-small''',
								    '''mobilevit_xs''': '''mobilevit-x-small''',
								    '''mobilevit_xxs''': '''mobilevit-xx-small''',
								    '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
								    '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
								    '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
								}

								print('''Pushing to the hub...'''				)
								__lowercase					     =				model_mapping[mobilevit_name]
								image_processor.push_to_hub(lowercase				,     organization='''apple'''				)
								model.push_to_hub(lowercase				,     organization='''apple'''				)


if __name__ == "__main__":
		__a    :	int         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--mobilevit_name""",
		    default="""mobilevit_s""",
		    type=str,
		    help=(
		        """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
		        """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
		    ),
		)
		parser.add_argument(
		    """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
		)
		parser.add_argument(
		    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
		)

		__a    :	Dict         =    parser.parse_args()
		convert_movilevit_checkpoint(
		    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)